The present disclosure related to quantitative imaging and analytics. More specifically, the present disclosure relates to systems and methods for analyzing pathologies utilizing quantitative imaging.
Imaging, particularly with safe and non-invasive methods, represents the most powerful methods for locating the disease origin, capturing its detailed pathology, directing therapy, and monitoring progression to health. Imaging is also an extremely valuable and low cost method to mitigate these human and financial costs by allowing for appropriate early interventions that are both less expensive and disruptive.
Enhanced imaging techniques have made medical imaging an essential component of patient care. Imaging is especially valuable because it provides spatially- and temporally-localized anatomic and functional information, using non- or minimally invasive methods. However, techniques to effectively utilize increasing spatial and temporal resolution are needed, both to exploit patterns or signatures in the data not readily assessed with the human eye as well as to manage the large magnitude of data in such a way as to efficiently integrate it into the clinical workflow. Without aid, the clinician has neither the time nor often the ability to effectively extract the information content which is available, and in any case generally interprets the information subjectively and qualitatively. Integrating quantitative imaging for individual patient management as well as clinical trials for therapy development requires a new class of decision support informatics tools to enable the medical community to fully exploit the capabilities made possible with the evolving and growing imaging modalities within the realities of existing work flows and reimbursement constraints.
Quantitative results from imaging methods have the potential to be used as biomarkers in both routine clinical care and in clinical trials, for example, in accordance with the widely accepted NIH Consensus Conference definition of a biomarker. In clinical practice, quantitative imaging are intended to (a) detect and characterize disease, before, during or after a course of therapy, and (b) predict the course of disease, with or without therapy. In clinical research, imaging biomarkers may be used in defining endpoints of clinical trials.
Quantification builds on imaging physics developments which have resulted in improvements of spatial, temporal, and contrast resolution as well as the ability to excite tissues with multiple energies/sequences, yielding diverse tissue-specific responses. These improvements thereby allow tissue discrimination and functional assessment, and are notably seen, for example, in spectral computed tomography (spectral CT), multi-contrast magnetic resonance imaging (multi-contrast MRI), ultrasound (US), and targeted contrast agent approaches with various imaging modalities. Quantitative imaging measures specific biological characteristics that indicate the effectiveness of one treatment over another, how effective a current treatment is, or what risk a patient is at should they remain untreated. Viewed as a measurement device, a scanner combined with image processing of the formed images has the ability to measure characteristics of tissue based on the physical principles relevant to a given imaging approach and how differing tissues respond to them. Though the image formation process differs widely across modalities, some generalizations help frame the overall assessment, though exceptions, nuances, and subtleties drive the real conclusions and until and unless they are considered some of the greatest opportunities are missed.
Imaging in the early phases of clinical testing of novel therapeutics contributes to the understanding of underlying biological pathways and pharmacological effects. It may also reduce the cost and time needed to develop novel pharmaceuticals and therapeutics. In later phases of development, imaging biomarkers may serve as important endpoints for clinical benefit. In all phases, imaging biomarkers may be used to select or stratify patients based on disease status, in order to better demonstrate therapeutic effect.
Continued improvement in the quality and effectiveness of medical care is needed to meet pressing demands as the population ages. For example,
#1 killer: Cardiovascular disease. Atherosclerosis is the largest culprit. 50% vascular surgeries do not benefit the patient, while some that need surgeries don't get them. Blood markers or just determining stenosis just holds the status quo. Current tools analyze the lumen, but atherosclerosis is a disease of the wall. Atherosclerosis is a disease of the wall rather than the blood. Results in 45% misclassification and inability to measure response to drugs or provide early intervention
#2 killer: Cancer. Lung cancer is the largest culprit. Early intervention reduces mortality, but uncertain diagnosis is a constraint. Lung cancer screening's potential for mortality reduction remains constrained by high burden of unnecessary costs. Just determining size of solid tissue holds the status quo. Current tools analyze solid tissue, but early cancer often manifests as sub-solid. Malignant cancer originates and spreads in sub-solid tissues. Results in 25% false positive rate which limits benefit by burdening the system with errors and inefficiencies
Medical imaging is involved in some way with half of that (unnecessary services, $210B, inefficiently delivered services, 130B, and missed prevention opportunities, 55B), either because it is part of the problem, could be tapped as part of the solution, or stands in the balance between these possibilities given its primary use in diagnosis, staging, and surveillance.
Limitations of current image analysis tools lead to high rate of misclassification due to limitations in subjective and qualitative assessment using only a fraction of the information content in the images that are taken.
There is growing evidence that MR has the potential to examine components that have been demonstrated to contribute to atherosclerosis [1-4]. The components include: lipid core distinguished by presence of lipids, intermixed with extracellular matrix fibers and/or necrotic tissue; fibrosis demonstrated by intimal presence of dense, homogeneous/organized collagen extracellular matrix with smooth muscle cells/fibroblasts embedded, but no appreciable lipid or necrotic tissue; calcification, including its distribution through the tissue; intra-plaque hemorrhage; and vascular permeability, contributed by inflammation demonstrated by accumulations of macrophages and lymphocytes in the deeper regions of the plaque that may bridge the neointima and the media of the vessel and/or vascular leak, composed of endothelial permeability, neovascularization, necrosis, and collagen breakdown. Likewise CT has also been applied to this measurement task, particularly given the speed and structural measurement capability [5-9]. See section on dual energy and multi-spectral CT for coronary imaging; these methods could also be applied to peripheral vessels.
When, as is currently the case for cardiovascular diseases, the available markers for stratifying patients into risk categories are gross and unreliable, clinical trials of new therapeutic entities will inevitably be larger, more expensive and more likely to fail than would be the case with validated quantitative biomarkers for risk. This is because poor assessment of risk leads to clinical trial populations that are insufficiently homogeneous and which include meaningful numbers of subjects not truly at elevated risk. As a result, very large enrollments are necessary to achieve a statistically significant difference in outcomes between treatment and control groups even for a highly effective therapeutic. A validated biomarker that accurately identifies a high risk population, thereby excluding low risk subjects from enrollment in a trial, will enable a statistically significant demonstration of therapeutic efficacy in a smaller and consequently far less expensive trial; intelligent use of such biomarkers will also produce a higher probability of trial success—a major benefit for drug manufacturers.
Even prior to initiation of clinical trials, validated biomarkers for plaque characterization can play an important role in drug development. In preclinical work, researchers need to analyze animal models to evaluate the efficacy of drugs on plaque severity before moving forward with expensive human clinical trials. The ability to measure plaque hallmarks would be a significant drug development project de-risking and cost avoidance advantage.
Once used for subject assessment in trials, an imaging study that provides a validated biomarker for cardiovascular risk can serve as a companion diagnostic for the therapeutic. This model, well established with the use of genetic markers for stratifying patient populations in oncology trials and clinical care, creates a mutually beneficial synergy between diagnostic and therapeutic: for the drug developer, the diagnostic takes risk and expense out of the drug development/clinical trial process in return for a more narrowly defined patient population in clinical use; at the same time, the diagnostic becomes clinically mandated in order to qualify patients for the therapeutic and for monitoring response. A companion imaging study that is used to evaluate patients and predict the individual's response to a therapeutic can be a valuable component in a total solution offered by a pharmaceutical company to clinicians.
The principal limitations of current techniques include the difficulty in interpreting raw pixel reconstructed intensity values using simplistic thresholding operators. One aspect of this is that the physical imaging modality intrinsically limits the degree to which the pixel values are correct manifestations of the object being imaged, for example due to the fact that a given point is actually spread or blurred according to the finite physical characteristics of the imaging. A second aspect is that a given imaging modality or setup is only interrogating tissue using a limited excitation, and that despite the utility of multi-contrast MR on the one hand, or multi-energy CT on the other, that there is always some degree of non-specific response in the produced signal.
The present disclosure is directed to addressing these two limitations in a manner which lends itself to effective implementation.